array([1.41421356,2.,3.,4.])

1.0

-1.0

array([1.,1.,2.,3.,1.,-1.,-2.])

array([1.,2.71828183,148.4131591])

array([10,15,17,26,13,19,12,11,21,24,14,23])

array([[10,15,17,26,13,19],

[12,11,21,24,14,23]])

array([[10,15,17,26],

[13,19,12,11],

[21,24,14,23]])

array([[10,15,17,26,13,19],

[12,11,21,24,14,23]])

[array([[10,15,17],

[12,11,21]]),

array([[26,13,19],

[24,14,23]])]

#examplesonuniversalfunctions

#importingimportantlibraries

importpandasaspd

importnumpyasnp

#numbersforwhichsquarerootwillbecalculated

np_sqrt=np.sqrt([2,4,9,16])

#squarerootvalues

np_sqrt

#importpi

fromnumpyimportpi

#trigonometricfunctions

np.cos(0)

#trigonometricfunctions

np.sin(pi/2)

#trigonometricfunctions

np.cos(pi)

#returntheflooroftheinputelementwise

np.floor([1.5,1.6,2.7,3.3,1.1,-0.3,-1.4])

#exponentialfunctionsforcomplexmathematicalcalculations

np.exp([0,1,5])

#importingtherequiredlibraries

importpandasaspd

importnumpyasnp

#shapemanipulationexamples

#createdthenewvariablethatisnew_cyclist_trials

new_cyclist_trials=np.array([[10,15,17,26,13,19],[12,11,21,24,14,23]])

#flattensthedataset

new_cyclist_trials.ravel()

#seentheoutputfornew_cyclist_trials

new_cyclist_trials

#changesorreshapesthedatasetinto3rowsand4columns

new_cyclist_trials.reshape(3,4)

#resizingthedatasetinto2rowsand6columns

new_cyclist_trials.resize(2,6)

#checkingtheoutputinto2rowsand6columns

new_cyclist_trials

#splittingthearrayintotwo

np.hsplit(new_cyclist_trials,2)

#createdthenewvariablecallednew_cyclist_1

array([12,11,21,24,14,23,12,11,21,24,14,23])

[12345]

array([0.,0.,0.])

array([[0.,0.,0.],

[0.,0.,0.],

[0.,0.,0.]])

array([[1.,1.,1.],

[1.,1.,1.],

[1.,1.,1.]])

array([[0.,0.,0.],

[0.,0.,0.]])

[01234567891011]

array([[0,1,2,3],

[4,5,6,7],

[8,9,10,11]])

array([1.,2.25,3.5,4.75,6.])

[01234567891011121314]

#createdthenewvariablecallednew_cyclist_1

new_cyclist_1=np.array([10,15,17,26,13,19])

#createdthenewvariablecallednew_cyclist_2

new_cyclist_2=np.array([12,11,21,24,14,23])

#stacksthearraytogether

#herewehavecombinedthenew_cyclist_1andnew_cyclist_2

np.hstack((new_cyclist_1,new_cyclist_2))

#firstnumpyarray

#thisistheregulararray:mentionarrayelementswithinthesquarebrackets

first_numpy_array=np.array([1,2,3,4,5])

#printingthe1starray

print(first_numpy_array)

#arraywithzeros

#zerosarray;mentionshapeofthearraywithinparentheses

#alwaysdoublebracketisused

array_with_zeros=np.zeros((3))

array_with_zeros

#arraywithzeros

array_with_zeros=np.zeros((3,3))

array_with_zeros

#arraywithones

array_with_ones=np.ones((3,3))

array_with_ones

#arraywithempty

array_with_empty=np.empty((2,3))

array_with_empty

#arraywitharangemathod

np_arange=np.arange(12)

#printingtherangebyarangemethod

print(np_arange)

#reshpemethodtochnageorcreteanarray

np_arange.reshape(3,4)

#linspceforlinearly,equalspaceddataelements

#herefirstelementis1,lastelementis6andthetotalnumberofequidistantelementis5

np_linspace=np.linspace(1,6,5)

#printingtheelements

np_linspace

#onedimenssionalarray

oneD_array=np.arange(15)

#printingtheonedimensionalarray

print(oneD_array)

#twodimensionalarray

[[01234]

[56789]

[1011121314]]

array([[[0,1,2],

[3,4,5],

[6,7,8]],

[[9,10,11],

[12,13,14],

[15,16,17]],

[[18,19,20],

[21,22,23],

[24,25,26]]])

0luxemburg

1norway

2japan

3swizerland

4unitedstates

5qatar

6iceland

7sweden

8singapore

9denmark

dtype:object

china1.1

japan2.2

america3.3

europe4.4

goa5.5

cst6.6

mumbai7.7

australia8.8

maldives9.9

dtype:float64

twoD_array=oneD_array.reshape(3,5)

#printingtheelementsoftwodimensioanlarray

print(twoD_array)

#threedimensionalarray

ThreeD_array=np.arange(27).reshape(3,3,3)

#printingtheelementsofthreedimensioanlarray

ThreeD_array

#createseriesfromndarray

#wehavecreatedthendarraythatmeansonedimensionalarrayforcountries

#wehavecreatedthenewvariablecallednp_country

np_country=np.array(['luxemburg','norway','japan','swizerland',

'unitedstates','qatar','iceland','sweden','singapore','denmark'])

#passndarrayasanarguement

np_country=pd.Series(np_country)

#printingtheelementsofthenp_country

#herethedatatypeidobject

print(np_country)

#createseriesfromdictonary

#evaluatecountriesandtheircorrespondinggdppercapitaandprintthemasaseries

#herethecountrieshasbeenpassedasanindexandgdpastheactualdatavalue

#dictionaryforcountriesandtheirgdp

dict_country_gdp=pd.Series([1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8,9.9],index=['china','japan',

'america','europe','goa',

'cst','mumbai','australia'

,'maldives'])

#thisdisplaysthegdpwiththecountries

#herethedatatypeisfloat

print(dict_country_gdp)

#printserieswithscalerinput

#herewehavegiventhescalarinput

#giventheindexlikea,b,c,d,e

scalar_Series=pd.Series(5.,index=['a','b','c','d','e'])

#herewehaveobservedtheindexasa,b,c,d,eandthedataofthatindex

#herethedatatypeisfloat

scalar_Series

a5.0

b5.0

c5.0

d5.0

e5.0

dtype:float64

1.1

china1.1

japan2.2

america3.3

europe4.4

goa5.5

dtype:float64

9.9

1.1

a11

b22

c33

d44

dtype:int64

a11

b22

c33

d44

dtype:int64

a11.0

b22.0

cNaN

dNaN

eNaN

fNaN

dtype:float64

#accessingelementsinseries

#datacanbeaccessedthroughdifferentfunctionslikeloc

#ilocbypassingthedataelementspositionorindexrange

#accesselementsintheseries

dict_country_gdp[0]

#accessfirstfivecountriesfromtheseries

#herewehavegiven0:5meansthatmeanswewillbegettingtotal5values

dict_country_gdp[0:5]

#lookupacountrybyanameoranindex

dict_country_gdp.loc['maldives']

#lookupbyanposition

dict_country_gdp.iloc[0]

#vectorizedoperationsinseries

#vectorizedoperationsareperformedbythedataelementsposition

#herewehaveaddtheseries

first_vector_Series=pd.Series([1,2,3,4],index=['a','b','c','d'])

second_vector_Series=pd.Series([10,20,30,40],index=['a','b','c','d'])

#herewehaveaddedfirst_vector_Seriesandsecond_vector_Series

first_vector_Series+second_vector_Series

#herewehaveonlyransecond_vector_Series

second_vector_Series=pd.Series([10,20,30,40],index=['a','b','c','d'])

#butherewehaveaddedboththefirst_vector_Seriesandsecond_vector_Seriesbutafteraddingtheresultwillbesame

first_vector_Series+second_vector_Series

#nowreplacefewindexeswithnewoneinsecondvectorseries

second_vector_Series=pd.Series([10,20,30,40],index=['a','b','e','f'])

#herethevalueforc,d,e,fisNANaswehaveremovedtheelemntsfromindex

first_vector_Series+second_vector_Series

#createdataframefromndarray

#creatingdataframefromdictofndarray

#importingnumpylibrary

importnumpyasnp

#createanndarrayswithyears

#createadictwiththendarray

np_array=np.array([2012,2008,2004,2006])

dict_ndarray={'year':np_array}

year

0 2012

1 2008

2 2004

3 2006

(83,42)

UPDATED ENTRY_DATE EVENT_LCL_DATE EVENT_LCL_TIME LOC_CITY_NAME LOC_STATE_NAME LOC_CNTRY_NAME RMK_TEXT EVENT_TYPE_DESC

0 No 19-FEB-16 19-FEB-16 00:45:00Z MARSHVILLE NorthCarolina NaN

AIRCRAFT

CRASHED

INTOTREES,

THE1

PERSONON

B...

1 No 19-FEB-16 18-FEB-16 23:55:00Z TAVERNIER Florida NaN

AIRCRAFT

ONLANDING

WENTOFF

THEENDOF

THERU...

2 No 19-FEB-16 18-FEB-16 22:14:00Z TRENTON NewJersey NaN

AIRCRAFT

ONFINAL

SUSTAINED

ABIRD

STRIKE,

LAN...

3 No 19-FEB-16 18-FEB-16 17:10:00Z ASHEVILLE NorthCarolina NaN

AIRCRAFT

LANDING,

GEAR

COLLAPSED,

ASHEVILLE...

4 No 19-FEB-16 18-FEB-16 00:26:00Z TALKEETNA Alaska NaN

AIRCRAFT

LANDING,

NOSEGEAR

COLLAPSED,

TALK...

5rows×42columns

Index(['UPDATED','ENTRY_DATE','EVENT_LCL_DATE','EVENT_LCL_TIME',

'LOC_CITY_NAME','LOC_STATE_NAME','LOC_CNTRY_NAME','RMK_TEXT',

'EVENT_TYPE_DESC','FSDO_DESC','REGIST_NBR','FLT_NBR','ACFT_OPRTR',

'ACFT_MAKE_NAME','ACFT_MODEL_NAME','ACFT_MISSING_FLAG',

'ACFT_DMG_DESC','FLT_ACTIVITY','FLT_PHASE','FAR_PART','MAX_INJ_LVL',

'FATAL_FLAG','FLT_CRW_INJ_NONE','FLT_CRW_INJ_MINOR',

'FLT_CRW_INJ_SERIOUS','FLT_CRW_INJ_FATAL','FLT_CRW_INJ_UNK',

'CBN_CRW_INJ_NONE','CBN_CRW_INJ_MINOR','CBN_CRW_INJ_SERIOUS',

'CBN_CRW_INJ_FATAL','CBN_CRW_INJ_UNK','PAX_INJ_NONE','PAX_INJ_MINOR',

'PAX_INJ_SERIOUS','PAX_INJ_FATAL','PAX_INJ_UNK','GRND_INJ_NONE',

'GRND_INJ_MINOR','GRND_INJ_SERIOUS','GRND_INJ_FATAL','GRND_INJ_UNK'],

dtype='object')

#passthedicttothenewdataframe

df_ndarray=pd.DataFrame(dict_ndarray)

#printingtheonedimensioanlarraythatisndarray

#itwillcreateanewdataframelistingalltheyears

df_ndarray

#importlibrary

importpandasaspd

importnumpyasnp

#readthefaathatisfederalavaiationauthoritydataset

df_faa_dataset=pd.read_csv("faa_ai_prelim.csv")

#viewtheshapeofthedataset

df_faa_dataset.shape

#viewthefirstfiveobservations

df_faa_dataset.head()

#viewallthecolumnspresentinthedataset

df_faa_dataset.columns

#howtocreateanewdataframewithonlyrequiredcolumns

df_analyze_dataset=df_faa_dataset[['ACFT_MAKE_NAME','LOC_STATE_NAME','ACFT_MODEL_NAME','RMK_TEXT',

'FLT_PHASE','EVENT_TYPE_DESC','FATAL_FLAG']]

#viewthetypeoftheobject

pandas.core.frame.DataFrame

ACFT_MAKE_NAME LOC_STATE_NAME ACFT_MODEL_NAME RMK_TEXT FLT_PHASE EVENT_TYPE_DESC FATAL_FLAG

0 BEECH NorthCarolina 36

AIRCRAFTCRASHED

INTOTREES,THE1

PERSONONB...

UNKNOWN

(UNK)

Accident Yes

1 VANS Florida RV7

AIRCRAFTON

LANDINGWENTOFF

THEENDOFTHE

RU...

LANDING

(LDG)

Incident NaN

2 CESSNA NewJersey 172

AIRCRAFTONFINAL

SUSTAINEDABIRD

STRIKE,LAN...

APPROACH

(APR)

Incident NaN

3 LANCAIR NorthCarolina 235

AIRCRAFTON

LANDING,GEAR

COLLAPSED,

ASHEVILLE...

LANDING

(LDG)

Incident NaN

4 CESSNA Alaska 172

AIRCRAFTON

LANDING,NOSE

GEARCOLLAPSED,

TALK...

LANDING

(LDG)

Incident NaN

C:\Users\Ankita\AppData\Local\Temp\ipykernel_26596\173567365.py:2:SettingWithCopyWarning:

AvalueistryingtobesetonacopyofaslicefromaDataFrame

Seethecaveatsinthedocumentation:https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ret

urning-a-view-versus-a-copy

df_analyze_dataset['FATAL_FLAG'].fillna(value='No',inplace=True)

ACFT_MAKE_NAME LOC_STATE_NAME ACFT_MODEL_NAME RMK_TEXT FLT_PHASE EVENT_TYPE_DESC FATAL_FLAG

0 BEECH NorthCarolina 36

AIRCRAFTCRASHED

INTOTREES,THE1

PERSONONB...

UNKNOWN

(UNK)

Accident Yes

1 VANS Florida RV7

AIRCRAFTON

LANDINGWENTOFF

THEENDOFTHE

RU...

LANDING

(LDG)

Incident No

2 CESSNA NewJersey 172

AIRCRAFTONFINAL

SUSTAINEDABIRD

STRIKE,LAN...

APPROACH

(APR)

Incident No

3 LANCAIR NorthCarolina 235

AIRCRAFTON

LANDING,GEAR

COLLAPSED,

ASHEVILLE...

LANDING

(LDG)

Incident No

4 CESSNA Alaska 172

AIRCRAFTON

LANDING,NOSE

GEARCOLLAPSED,

TALK...

LANDING

(LDG)

Incident No

(83,7)

(78,7)

#viewthetypeoftheobject

type(df_analyze_dataset)

#viewfirstfiveobservations

df_analyze_dataset.head()

#replaceallNaNforfatalflagwith'no'

df_analyze_dataset['FATAL_FLAG'].fillna(value='No',inplace=True)

#nowviewfirstfiveobservations

df_analyze_dataset.head()

#viewtheshapeofthedataset

df_analyze_dataset.shape

#dropthevalueswhereACFT_MAKE_NAMEthatisaircraftmakenameisnotavailable

df_final_dataset=df_analyze_dataset.dropna(subset=['ACFT_MAKE_NAME'])

#nowviewthenewshapeofthedataset

df_final_dataset.shape

#groupbyaircraftname

aircraftType=df_final_dataset.groupby('ACFT_MAKE_NAME')

#viewthembyaircraftusingsizemethod

aircraftType.size()

#thisisthelistofalltheaircraftstypesandthenumberoftimestheyappearinthedataset.

ACFT_MAKE_NAME

AEROCOMMANDER1

AERONCA1

AEROSTARINTERNATIONAL1

AIRBUS1

BEECH9

BELL2

BOEING3

CESSNA23

CHAMPION2

CHRISTEN1

CONSOLIDATEDVULTEE1

EMBRAER1

ENSTROM1

FAIRCHILD1

FLIGHTDESIGN1

GLOBE1

GREATLAKES1

GRUMMAN1

GULFSTREAM1

HUGHES1

LANCAIR2

MAULE1

MOONEY4

NORTHAMERICAN1

PIPER10

PITTS1

SAAB1

SABRELINER1

SOCATA2

VANS1

dtype:int64

FATAL_FLAG

No71

Yes7

dtype:int64

ACFT_MAKE_NAME LOC_STATE_NAME ACFT_MODEL_NAME RMK_TEXT FLT_PHASE EVENT_TYPE_DESC FATAL_FLAG

0 BEECH NorthCarolina 36

AIRCRAFT

CRASHEDINTO

TREES,THE1

PERSONONB...

UNKNOWN

(UNK)

Accident Yes

53 PIPER Florida PA28

AIRCRAFT

CRASHEDUNDER

UNKNOWN

CIRCUMSTANCES.

...

UNKNOWN

(UNK)

Accident Yes

55 FLIGHTDESIGN California CTLS

AIRCRAFT

CRASHEDUNDER

UNKNOWN

CIRCUMSTANCES

A...

UNKNOWN

(UNK)

Accident Yes

79 NORTHAMERICAN Arizona F51

AIRCRAFT

CRASHEDUNDER

UNKNOWN

CIRCUMSTANCES,

...

UNKNOWN

(UNK)

Accident Yes

80 CHAMPION California 8KCAB

N9872R,BEECHM35

AIRCRAFT,AND

N5057G,BELLAN...

UNKNOWN

(UNK)

Accident Yes

81 BEECH California 35

N9872R,BEECHM35

AIRCRAFT,AND

N5057G,BELLAN...

UNKNOWN

(UNK)

Accident Yes

82 CESSNA Alabama 182

N784CPAIRCRAFT

CRASHEDINTOA

WOODEDAREA

NEA...

UNKNOWN

(UNK)

Accident Yes

#nowgroupthedatasetbyfatalflag

fatalAccidents=df_final_dataset.groupby('FATAL_FLAG')

#viewthetotalaccidentssize

fatalAccidents.size()

#selecttheaccidentswithfatalitywithfatalflagyes

accidents_with_fatality=fatalAccidents.get_group('Yes')

#viewtheaccidentswithfatality

accidents_with_fatality

#importinglibraries

importpandasaspd

importnumpyasnp

#readthedatafromcsvfilefiredepartmentofnewyorkcitythatisFDNY

<boundmethodNDFrame.describeofFacilityName\

0FacilityName

1Engine4/Ladder15

2Engine10/Ladder10

3Engine6

4Engine7/Ladder1/Battalion1/ManhattanBoroug...

.....

214Engine162/Ladder82/Battalion23

215Engine167/Ladder87

216Engine164/Ladder84

217Engine168/EMSStation23

218Engine151/Ladder76

FacilityAddressBorough

0FacilityAddressBorough

142SouthStreetManhattan

2124LibertyStreetManhattan

349BeekmanStreetManhattan

4100-104DuaneStreetManhattan

........

214256NelsonAvenueStatenIsland

215345AnnadaleRoadStatenIsland

2161560DrumgooleRoadWestStatenIsland

2171100RossvilleAvenueStatenIsland

2187219AmboyRoadStatenIsland

[219rowsx3columns]>

FacilityName FacilityAddress Borough

0 FacilityName FacilityAddress Borough

1 Engine4/Ladder15 42SouthStreet Manhattan

2 Engine10/Ladder10 124LibertyStreet Manhattan

3 Engine6 49BeekmanStreet Manhattan

4 Engine7/Ladder1/Battalion1/ManhattanBoroug... 100-104DuaneStreet Manhattan

FacilityName FacilityAddress Borough

0 Engine4/Ladder15 42SouthStreet Manhattan

1 Engine10/Ladder10 124LibertyStreet Manhattan

2 Engine6 49BeekmanStreet Manhattan

3 Engine7/Ladder1/Battalion1/ManhattanBoroug... 100-104DuaneStreet Manhattan

4 Ladder8 14NorthMooreStreet Manhattan

FacilityName FacilityAddress Borough

count 218 218 218

unique 218 218 5

top Engine4/Ladder15 42SouthStreet Brooklyn

freq 1 1 66

Index(['FacilityName','FacilityAddress','Borough'],dtype='object')

RangeIndex(start=0,stop=218,step=1)

#readthedatafromcsvfilefiredepartmentofnewyorkcitythatisFDNY

df_fdny_csv_data_raw=pd.read_csv("FDNY.csv")

#viewcontentofthedata

df_fdny_csv_data_raw.describe

#viewfirstfiverecords

df_fdny_csv_data_raw.head(5)

#skipthefirstrowfromthedataset

df_fdny_csv_data=pd.read_csv('FDNY.csv',skiprows=1)

#viewfirstfiverecordsfromthefixeddataset

df_fdny_csv_data.head()

#viewdatastatisticsusingdescribe

df_fdny_csv_data.describe()

#viewcoulmnsofthedataset

df_fdny_csv_data.columns

#viewindexofthedataset

df_fdny_csv_data.index

#countnumberofrecords

FacilityName218

FacilityAddress218

Borough218

dtype:int64

FacilityNameobject

FacilityAddressobject

Boroughobject

dtype:object

Borough

Bronx34

Brooklyn66

Manhattan48

Queens50

StatenIsland20

dtype:int64

#countnumberofrecords

#herearethetotalnumberoffiredepartmentfacilities

df_fdny_csv_data.count()

#viewdatatypes

df_fdny_csv_data.dtypes

#selectFDNYinformationboroughwise

groupby_borough=df_fdny_csv_data.groupby('Borough')

#viewFDNYinformationboroughwise

#thisarethenumberoffiredepartmentsfacilitiesineachborough

groupby_borough.size()

#selectFDNYinformationforManhattan

fdny_info_Manhattan=groupby_borough.get_group('Manhattan')

#viewFDNYinformationforManhattan

fdny_info_Manhattan

FacilityName FacilityAddress Borough

0 Engine4/Ladder15 42SouthStreet Manhattan

1 Engine10/Ladder10 124LibertyStreet Manhattan

2 Engine6 49BeekmanStreet Manhattan

3 Engine7/Ladder1/Battalion1/ManhattanBoroug... 100-104DuaneStreet Manhattan

4 Ladder8 14NorthMooreStreet Manhattan

5 Engine9/Ladder6 75CanalStreet Manhattan

6 Engine15/Ladder18/Battalion4 25PittStreet Manhattan

7 Engine28/Ladder11 222East2ndStreet Manhattan

8 Engine5 340East14thStreet Manhattan

9 Engine55 363BroomeStreet Manhattan

10 Ladder20/Division1 253LafayetteStreet Manhattan

11 Engine24/Ladder5/Battalion2 227-296thAvenue Manhattan

12 Engine33/Ladder9 42GreatJonesStreet Manhattan

13 Ladder3/Battalion6 108East13thStreet Manhattan

14 Squad18 132West10thStreet Manhattan

15 Engine34/Ladder21 440West38thStreet Manhattan

16 Engine26 220West37thStreet Manhattan

17 Engine3/Ladder12/Battalion7 150West19thStreet Manhattan

18 Engine1/Ladder24 142-46West31stStreet Manhattan

19 Engine14 14East18thStreet Manhattan

20 Engine16/Ladder7 234East29thStreet Manhattan

21 Engine21 238East40thStreet Manhattan

22 Engine54/Ladder4/Battalion9 7828thAvenue Manhattan

23 Engine23 215West58thStreet Manhattan

24 Rescue1 530West43rdStreet Manhattan

25 Engine40/Ladder35 131AmsterdamAvenue Manhattan

26 Ladder25/DistrictOffice4/Division3 205-207West77thStreet Manhattan

27 Engine74 120West83rdStreet Manhattan

28 Engine65 33West43rdStreet Manhattan

29 Engine8/Ladder2/Battalion8 167East51stStreet Manhattan

30 Engine39/Ladder16 157-59East67thStreet Manhattan

31 Engine44 221East75thStreet Manhattan

32 Engine22/Ladder13/Battalion10 159East85thStreet Manhattan

33 Engine58/Ladder26 13675thAvenue Manhattan

34 Engine53/Ladder43 1836-463rdAvenue Manhattan

35 Engine91 242East111thStreet Manhattan

36 Engine35/Ladder14 22823rdAvenue Manhattan

37 Engine76/Ladder22/Battalion11 145-51West100thStreet Manhattan

38 Engine47 502West113thStreet Manhattan

39 Engine59/Ladder30 111West133rdStreet Manhattan

40 Engine37/Ladder40 415West125thStreet Manhattan

41 Engine69/Ladder28/Battalion16 248West143rdStreet Manhattan

42 Engine80/Ladder23 503West139thStreet Manhattan

43 Engine84/Ladder34 513West161stStreet Manhattan

44 Engine67 518West170thStreet Manhattan

45 Engine93/Ladder45/Battalion13 515West181stStreet Manhattan

46 Engine95/Ladder36 29VermilyeaAvenue Manhattan

47 Marine1 LittleWest12thStreet/HudsonRiver Manhattan

#importimportantlibraries

importmatplotlib.pyplotasplt

%matplotlibinline

#datafromohiostate,leadingcausesofdeathinthestatefromyear2012

#causesofdeath

cause='ChronicDiseases','UnintentionalInjuries','Alzheimers','InfluenzaandPneumonia',

array([24.,6.])

'Sepsis','Others'

#percentile

percentile=[62,5,4,2,1,26]

#setthepiechartplotproperties

#setfiguresize

plt.figure(figsize=(10,10))

#explodethelargestpieinthedataset

#explodemeansgapbetweenthepiechartblocks

explode=(0.05,0,0,0,0,0)

#setpiechartproperties

#autopct='%1.1f%%'-thisisforbettervisualization

plt.pie(percentile,labels=cause,explode=explode,autopct='%1.1f%%',startangle=70)

#setaxisequaltodrawpieascircle

plt.axis('equal')

#settitleofthepiechart

plt.title('OhioState-2012:LeadingCausesofDeath')

#showtheplotthatispiechart

plt.show()

#importimportantlibrraies

importnumpyasnp

fromscipyimportlinalg

#testhas30questionsandworth150marks

#trueandfalsequestionsworth4markseach

#multiplechoicequestionsworth9markseach

#letxisthenumberoftrue/falsequestions

#letyisthenumberofmultiplechoicequestions

#(x+y=30)

#(4x+9y=150)

testQuestionVariable=np.array([[1,1],[4,9]])

testQuestionValue=np.array([30,150])

#uselinalgfunctionofscipy

#usesolvemethodtosolvethelinearequationandfindvalueforxandy

linalg.solve(testQuestionVariable,testQuestionValue)

#importingrequiredlibraries

Unnamed:0 TVAdBudget($) RadioAdBudget($) NewspaperAdBudget($) Sales($)

0 1 230.1 37.8 69.2 22.1

1 2 44.5 39.3 45.1 10.4

2 3 17.2 45.9 69.3 9.3

3 4 151.5 41.3 58.5 18.5

4 5 180.8 10.8 58.4 12.9

1000

(200,5)

Index(['Unnamed:0','TVAdBudget($)','RadioAdBudget($)',

'NewspaperAdBudget($)','Sales($)'],

dtype='object')

NewspaperAdBudget($) Sales($) RadioAdBudget($)

0 69.2 22.1 37.8

1 45.1 10.4 39.3

2 69.3 9.3 45.9

3 58.5 18.5 41.3

4 58.4 12.9 10.8

Sales($)

0 22.1

1 10.4

2 9.3

3 18.5

4 12.9

(200,3)

(200,1)

#importingrequiredlibraries

importpandasaspd

#importingtheadvertisingdataset

df_adv_data=pd.read_csv("Advertising.csv")

#viewthetop5records

df_adv_data.head()

#viewthedatasetsize

df_adv_data.size

#viewtheshapeofthedataset

df_adv_data.shape

#viewthecolumnsofthedataset

df_adv_data.columns

#createafeatureobjectfromthecolumns

X_feature=df_adv_data[['NewspaperAdBudget($)','Sales($)','RadioAdBudget($)']]

#viewfeatureobject

X_feature.head()

#createtargetobjectfromsalescolumnwhichisaresponseinthedataset

Y_target=df_adv_data[['Sales($)']]

#viewthetargetobject

Y_target.head()

#viewthefeatureobjectshape

X_feature.shape

#viewthetargetobjectshape

Y_target.shape

#splittestandtrainingdata

#bydefault75%trainingdataand25%testingdata

#splitastrainandtestdataset

(150,3)

(150,1)

(50,3)

(50,1)

LinearRegression()

[-3.55271368e-15]

[[0.00000000e+001.00000000e+00-9.04990991e-17]]

array([[23.8],

[16.6],

[9.5],

[14.8],

[17.6],

[25.5],

[16.9],

[12.9],

[10.5],

[17.1],

[14.5],

[11.3],

[17.4],

[16.7],

[13.4],

[15.9],

[12.9],

[12.8],

[9.5],

[18.4],

[10.7],

[12.5],

[8.5],

[11.5],

[11.9],

[14.9],

[10.1],

[18.9],

[19.6],

[15.9],

[23.2],

[11.9],

[17.3],

[11.7],

[20.2],

[15.5],

[11.5],

[11.],

[22.3],

[7.6],

[5.3],

[8.7],

[6.7],

[19.],

[5.5],

[14.6],

[21.5],

[22.6],

[19.7]])

fromsklearn.model_selectionimporttrain_test_split

x_train,x_test,y_train,y_test=train_test_split(X_feature,Y_target,random_state=1)

#viewtheshapeofthetrainandtestdatasetsforbothfeatureandresponse

print(x_train.shape)

print(y_train.shape)

print(x_test.shape)

print(y_test.shape)

#linearregressionmodel

#thisisthecodetocreatealinearregressionmodelwhichwillpredictthesalesoutcomeforany

#newdata

fromsklearn.linear_modelimportLinearRegression

linreg=LinearRegression()

linreg.fit(x_train,y_train)

#printtheinterceptandcoeffiecients

print(linreg.intercept_)

print(linreg.coef_)

#prediction

y_pred=linreg.predict(x_test)

y_pred

#importrequiredlibrariesforcalculatingMSEthatismeansquareerror

fromsklearnimportmetrics

1.833179602832606e-15

['html',

<htmlitemscope=""itemtype="http://schema.org/WebPage"lang="en-IN"><head><metacontent="text/html;charset=u

tf-8"http-equiv="Content-Type"/><metacontent="/images/branding/googleg/1x/googleg_standard_color_128dp.png"i

temprop="image"/><title>Google</title><scriptnonce="a9fEDiXswLfU_nPTDCFxVQ">(function(){window.google={kEI:'W5

6uY5C4Ou7S1sQPn_6M6Ag',kEXPI:'0,1359409,6059,206,4804,2316,383,246,5,1129120,1197703,698,380090,16114,28682,110

9,21324,1361,12318,17581,4998,13228,3847,10623,22740,2370,2711,1593,1279,2742,149,1103,841,2196,4101,108,4011,2

023,2297,14670,3227,2845,7,4774,20215,4086,4695,1851,15756,3,576,1014,1,5444,149,11323,2652,4,1528,2304,7039,22

023,5708,7355,13660,4437,16786,5824,2533,4094,4052,3,3541,1,14263,27891,2,14022,2715,23024,5679,1021,2377,28745

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5784,3995,21391,388,9543,4832,23189,3314,15605,4532,14,82,3890,757,8686,6194,679,1622,782,997,668,3183,1125,174

6,2041,4264,937,5903,1742,813,1224,10,280,2350,1497,2,32,531,909,82,1001,1094,350,90,399,96,426,1034,42,291,225

9,410,1649,1410,20,3,261,606,136,789,420,915,290,135,1304,246,951,841,1378,947,857,74,29,203,1561,1422,149,234,

71,3,539,77,9,512,326,226,156,327,9,323,50,53,166,6,258,767,184,394,1087,164,1088,5,342,460,433,284,130,108,463

,2,215,85,161,72,119,10,447,174,1068,69,857,340,500,62,118,1274,97,1,1139,1570,49,8,538,529,77,101,38,425,36,12

,105,1372,532,890,345,278,427,1199,104,191,218,544,196,3222,111,4,53,4,40,2,355,17,179,87,1689,417,654,620,756,

5272650,4190,8799060,3311,141,795,19735,1,1,346,3645,5,17,23946817,4042143,1964,16672,3406,5595,11,3834,6974,22

4',kBL:'Dl4b'};google.sn='webhp';google.kHL='en-IN';})();(function(){

varf=this||self;varh,k=[];functionl(a){for(varb;a&&(!a.getAttribute||!(b=a.getAttribute("eid")));)a=a.pare

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de;returnb}

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4")+"?atyp=i&ct="+a+"&cad="+b+e+"&zx="+Date.now()+d;/^http:/i.test(c)&&"https:"===window.location.protocol&&(go

ogle.ml&&google.ml(Error("a"),!1,{src:c,glmm:1}),c="");returnc};h=google.kEI;google.getEI=l;google.getLEI=m;go

ogle.ml=function(){returnnull};google.log=function(a,b,c,d,g){if(c=n(a,b,c,d,g)){a=newImage;vare=k.length;k[

e]=a;a.onerror=a.onload=a.onabort=function(){deletek[e]};a.src=c}};google.logUrl=n;}).call(this);(function(){g

oogle.y={};google.sy=[];google.x=function(a,b){if(a)varc=a.id;else{doc=Math.random();while(google.y[c])}googl

e.y[c]=[a,b];return!1};google.sx=function(a){google.sy.push(a)};google.lm=[];google.plm=function(a){google.lm.p

ush.apply(google.lm,a)};google.lq=[];google.load=function(a,b,c){google.lq.push([[a],b,c])};google.loadAll=func

tion(a,b){google.lq.push([a,b])};google.bx=!1;google.lx=function(){};}).call(this);google.f={};(function(){

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())},!0);document.documentElement.addEventListener("click",function(b){vara;a:{for(a=b.target;a&&a!==document.

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</style><style>body,td,a,p,.h{font-family:arial,sans-serif}body{margin:0;overflow-y:scroll}#gog{padding:3px8p

x0}td{line-height:.8em}.gac_mtd{line-height:17px}form{margin-bottom:20px}.h{color:#1558d6}em{font-weight:bold

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s-serif}.ds{display:inline-box;display:inline-block;margin:3px04px;margin-left:4px}input{font-family:inherit}

body{background:#fff;color:#000}a{color:#4b11a8;text-decoration:none}a:hover,a:active{text-decoration:underline

}.fla{color:#1558d6}a:visited{color:#4b11a8}.sblc{padding-top:5px}.sblca{display:block;margin:2px0;margin-le

ft:13px;font-size:11px}.lsbb{background:#f8f9fa;border:solid1px;border-color:#dadce0#70757a#70757a#dadce0;h

eight:30px}.lsbb{display:block}#WqQANba{display:inline-block;margin:012px}.lsb{background:url(/images/nav_log

o229.png)0-261pxrepeat-x;border:none;color:#000;cursor:pointer;height:30px;margin:0;outline:0;font:15pxaria

l,sans-serif;vertical-align:top}.lsb:active{background:#dadce0}.lst:focus{outline:none}</style><scriptnonce="a

9fEDiXswLfU_nPTDCFxVQ">(function(){window.google.erd={jsr:1,bv:1702,de:true};

varh=this||self;vark,l=null!=(k=h.mei)?k:1,n,p=null!=(n=h.sdo)?n:!0,q=0,r,t=google.erd,v=t.jsr;google.ml=fun

ction(a,b,d,m,e){e=void0===e?2:e;b&&(r=a&&a.message);if(google.dl)returngoogle.dl(a,e,d),null;if(0>v){window.

console&&console.error(a,d);if(-2===v)throwa;b=!1}elseb=!a||!a.message||"Errorloadingscript"===a.message||q

>=l&&!m?!1:!0;if(!b)returnnull;q++;d=d||{};b=encodeURIComponent;varc="/gen_204?atyp=i&ei="+b(google.kEI);goog

le.kEXPI&&(c+="&jexpid="+b(google.kEXPI));c+="&srcpg="+b(google.sn)+"&jsr="+b(t.jsr)+"&bver="+b(t.bv);varf=a.l

ineNumber;void0!==f&&(c+="&line="+f);varg=

a.fileName;g&&(0<g.indexOf("-extension:/")&&(e=3),c+="&script="+b(g),f&&g===window.location.href&&(f=document.

importnumpyasnp

#calculatetheMSEthatismeansquareerror

print(np.sqrt(metrics.mean_squared_error(y_test,y_pred)))

#importrequiredlibrraies

#herebs4isthelibraryfromwhichweareimportingtheBeautifulSoup

#wearealsoimportingrequests

frombs4importBeautifulSoup

importrequests

#urlforwebscrapping

url='https://www.google.com'

#accesstheurlwithrequestobject

result=requests.get(url)

#scrapthewebpagecontent

webpage=result.content

#createasoupobjectforparsingthewebpageusingthehtmlparser

#heresiisthesoupobjectcreatedforparsingwebpageusingHTMLparser.

sl_soup=BeautifulSoup(webpage,'html.parser')

#closetheresultobject

result.close()

#viewthecontentofthesoupobject

sl_soup.contents

documentElement.outerHTML.split("\n")[f],c+="&cad="+b(f?f.substring(0,300):"Noscriptfound.")));c+="&jsel="+e;

for(varuind)c+="&",c+=b(u),c+="=",c+=b(d[u]);c=c+"&emsg="+b(a.name+":"+a.message);c=c+"&jsst="+b(a.stack||"

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m,e){r!==a&&(a=einstanceofError?e:Error(a),void0===d||"lineNumber"ina||(a.lineNumber=d),void0===b||"fileNa

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11)||-1!==a.message.indexOf("Scripterror")?3:0));r=null;p&&q>=l&&(window.onerror=null)};})();</script></head><

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o"src="/images/branding/googlelogo/1x/googlelogo_white_background_color_272x92dp.png"style="padding:28px014

px"width="272"/><br/><br/></div><formaction="/search"name="f"><tablecellpadding="0"cellspacing="0"><trval

ign="top"><tdwidth="25%"></td><tdalign="center"nowrap=""><inputname="ie"type="hidden"value="ISO-8859-1"/

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type="hidden"/><inputname="bih"type="hidden"/><divclass="ds"style="height:32px;margin:4px0"><inputautocom

plete="off"class="lst"maxlength="2048"name="q"size="57"style="margin:0;padding:5px8px06px;vertical-alig

n:top;color:#000"title="GoogleSearch"value=""/></div><brstyle="line-height:0"/><spanclass="ds"><spanclass

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his.form.q.value){this.checked=1;if(this.form.iflsig)this.form.iflsig.disabled=false;}

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vanced_search?hl=en-IN&authuser=0">Advancedsearch</a></td></tr></table><inputid="gbv"name="gbv"type="hi

dden"value="1"/><scriptnonce="a9fEDiXswLfU_nPTDCFxVQ">(function(){vara,b="1";if(document&&document.getElemen

tById)if("undefined"!=typeofXMLHttpRequest)b="2";elseif("undefined"!=typeofActiveXObject){varc,d,e=["MSXML2

.XMLHTTP.6.0","MSXML2.XMLHTTP.3.0","MSXML2.XMLHTTP","Microsoft.XMLHTTP"];for(c=0;d=e[c++];)try{newActiveXObjec

t(d),b="2"}catch(h){}}a=b;if("2"==a&&-1==location.search.indexOf("&gbv=2")){varf=google.gbvu,g=document.getEle

mentById("gbv");g&&(g.value=a);f&&window.setTimeout(function(){location.href=f},0)};}).call(this);</script></fo

rm><divid="gac_scont"></div><divstyle="font-size:83%;min-height:3.5em"><br/><divid="prm"><style>.szppmdbYutt

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le}</style><divclass="szppmdbYutt__middle-slot-promo"data-ved="0ahUKEwjQ0fq-86D8AhVuqZUCHR8_A40QnIcBCAQ"><spa

n>Lookbackontheyearbyexploringthe</span><aclass="NKcBbd"href="https://www.google.com/url?q=https://ab

out.google/stories/year-in-search/%3Futm_source%3Dgoogle%26utm_medium%3Dhpp%26utm_campaign%3DIndia_yis22&so

urce=hpp&id=19033112&ct=3&usg=AOvVaw1jtvJixqyHY8bvEFRJBZJz&sa=X&ved=0ahUKEwjQ0fq-86D8AhVuqZ

UCHR8_A40Q8IcBCAU"rel="nofollow">Searchtrendsof2022</a></div></div><divid="gws-output-pages-elements-homep

age_additional_languages__als"><style>#gws-output-pages-elements-homepage_additional_languages__als{font-size:s

mall;margin-bottom:24px}#SIvCob{color:#3c4043;display:inline-block;line-height:28px;}#SIvCoba{padding:03px;}.

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ivid="SIvCob">Googleofferedin:<ahref="https://www.google.com/setprefs?sig=0_slLVMBe6sHLLGQLOG94MzmSLnNs%3

D&hl=hi&source=homepage&sa=X&ved=0ahUKEwjQ0fq-86D8AhVuqZUCHR8_A40Q2ZgBCAc"></a><ahref="htt

ps://www.google.com/setprefs?sig=0_slLVMBe6sHLLGQLOG94MzmSLnNs%3D&hl=bn&source=homepage&sa=X&ve

d=0ahUKEwjQ0fq-86D8AhVuqZUCHR8_A40Q2ZgBCAg">ÊÚ¢ÑÚ</a><ahref="https://www.google.com/setprefs?sig=0_slLVMBe6sHL

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